Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining.


Journal

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
ISSN: 2694-0604
Titre abrégé: Annu Int Conf IEEE Eng Med Biol Soc
Pays: United States
ID NLM: 101763872

Informations de publication

Date de publication:
Jul 2019
Historique:
entrez: 18 1 2020
pubmed: 18 1 2020
medline: 25 2 2020
Statut: ppublish

Résumé

Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular risk in patients with type 2 diabetes, we applied process mining techniques based on the principles of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk. This enables the extraction of meaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve the management of cardiovascular diseases in type 2 diabetes patients.

Identifiants

pubmed: 31945911
doi: 10.1109/EMBC.2019.8856507
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

341-344

Auteurs

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Classifications MeSH